NVIDIA · Ampere
Best AI Models for NVIDIA GeForce RTX 3060 Laptop GPU (6 GB)
Runs only small models well; 7–9B only just fits.
Laptop RTX 3060 has 6 GB (desktop has 12 GB), so ~7B Q4 with moderate context is the practical ceiling. Real speed depends on the chassis power limit (60-115 W).
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 67.0 t/s | 67.0 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 76.9 t/s | 76.9 tok/s | Q4_K_M · 5.0 GB | |
| 3 | Gemma 4 E2B ITGoogle Q4_K_M · 3.5 GB · 63.7 t/s | 63.7 tok/s | Q4_K_M · 3.5 GB | |
| 4 | Spark X2.6 4BXHToken Q4_K_M · 4.3 GB · 73.8 t/s | 73.8 tok/s | Q4_K_M · 4.3 GB | |
| 5 | MiniCPM5 2BOpenBMB Q4_K_M · 2.5 GB · 115 t/s | 115 tok/s | Q4_K_M · 2.5 GB |
| # | Model | Speed | Reasoning Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Q4_K_M · 5.0 GB · 76.9 t/s | 76.9 tok/s | Q4_K_M · 5.0 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 67.0 t/s | 67.0 tok/s | overall | Q4_K_M · 3.4 GB |
| 3 | Gemma 4 E2B ITGoogle Q4_K_M · 3.5 GB · 63.7 t/s | 63.7 tok/s | Q4_K_M · 3.5 GB | |
| 4 | Spark X2.6 4BXHToken Q4_K_M · 4.3 GB · 73.8 t/s | 73.8 tok/s | Q4_K_M · 4.3 GB | |
| 5 | MiniCPM5 2BOpenBMB Q4_K_M · 2.5 GB · 115 t/s | 115 tok/s | Q4_K_M · 2.5 GB |
| # | Model | Speed | Long context Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 67.0 t/s | 67.0 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Gemma 4 E2B ITGoogle Q4_K_M · 3.5 GB · 63.7 t/s | 63.7 tok/s | Q4_K_M · 3.5 GB | |
| 3 | Spark X2.6 4BXHToken Q4_K_M · 4.3 GB · 73.8 t/s | 73.8 tok/s | Q4_K_M · 4.3 GB | |
| 4 | MiniCPM5 2BOpenBMB Q4_K_M · 2.5 GB · 115 t/s | 115 tok/s | Q4_K_M · 2.5 GB | |
| 5 | LFM3 1.7BLiquid AI Q4_K_M · 2.1 GB · 152 t/s | 152 tok/s | Q4_K_M · 2.1 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 67.0 t/s | 67.0 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 179 t/s | 179 tok/s | Q4_K_M · 5.5 GB | |
| 3 | MiniCPM5 2BOpenBMB Q4_K_M · 2.5 GB · 115 t/s | 115 tok/s | Q4_K_M · 2.5 GB | |
| 4 | Spark X2.6 4BXHToken Q4_K_M · 4.3 GB · 73.8 t/s | 73.8 tok/s | Q4_K_M · 4.3 GB | |
| 5 | Gemma 4 E2B ITGoogle Q4_K_M · 3.5 GB · 63.7 t/s | 63.7 tok/s | Q4_K_M · 3.5 GB |
| # | Model | Speed | Vision Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 67.0 t/s | 67.0 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 76.9 t/s | 76.9 tok/s | Q4_K_M · 5.0 GB | |
| 3 | LFM3 Omni 2BLiquid AI Q4_K_M · 2.4 GB · 129 t/s | 129 tok/s | Q4_K_M · 2.4 GB | |
| 4 | MiniCPM V 4.7 1BOpenBMB Q4_K_M · 1.6 GB · 171 t/s | 171 tok/s | Q4_K_M · 1.6 GB | |
| 5 | Cosmos Reason2 2BNVIDIA Q4_K_M · 3.5 GB · 136 t/s | 136 tok/s | Q4_K_M · 3.5 GB |
The RTX 3060 Laptop GPU runs only small models well. Ampere generation, released 2021.
RTX 3060 Laptop GPU strengths and weaknesses
Strengths
- Runs 7–9B models: Qwen3.5 9B at 39.6 tok/s in 5.8 GB
- Low power draw: 115 W board power; a 250 W PSU is enough
Weaknesses
- 14B models don't fit: Qwen3 14B needs 11.9 GB at Q4 vs 6 GB here
- Little room for context: Qwen3.5 9B uses 5.8 of 6 GB, 0.2 GB left
- No FP8 or NVFP4: Ampere predates both; use GGUF, AWQ or GPTQ builds
Cards people weigh against the RTX 3060 Laptop GPU, each with the one reason it is worth a look.
Output speed depends on memory bandwidth (336 GB/s). Grey rows need more than 6 GB.
- 39.6tok/s
- 14BQwen3 14B · not at 4-bitneeds 11.9 GB
- 27–31BQwen3.8 27B · not at 4-bitneeds 17.9 GB
- 35B MoEQwen3.6 35B A3B · not at 4-bitneeds 22.0 GB
- 70BLlama 3.3 70B Instruct · not at 4-bitneeds 51.3 GB
- 120B MoEGPT OSS 120B · not at 4-bitneeds 70.8 GB
Estimated speeds: Q4_K_M, 4K context, one user. How we estimate
All 738 models on the RTX 3060 Laptop GPU
Featured onlyBest quant that fits, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
| Q3_K_S | 6.0 GB | 53.5 t/s | 131K | C44, Usable | ||
BioMistral 7BChat | Q3_K_M | 5.9 GB | 54.9 t/s | 33K | C44, Usable | |
| Q3_K_M | 6.0 GB | 53.3 t/s | 33K | C44, Usable | ||
| Q2_K | 5.9 GB | 54.9 t/s | 131K | C44, Usable | ||
| IQ3_XXS | 6.0 GB | 63.9 t/s | — | C44, Usable | ||
DeepSeek Coder v2 Lite InstructChatCode | IQ2_S | 5.7 GB | 170.2 t/s | 164K | B62, Decent | |
Llama 2 7B HFChat | Q3_K_L | 5.9 GB | 45.2 t/s | 4K | C44, Usable | |
| Q2_K | 5.7 GB | 41.5 t/s | 4K | B55, Decent | ||
Falcon Mamba 7BChat | Q3_K_S | 6 GB | 62.4 t/s | — | C44, Usable | |
Qwen 14B ChatChat | IQ2_XS | 6.0 GB | 46.7 t/s | 8K | C44, Usable | |
| Q2_K | 5.7 GB | 155.3 t/s | 262K | B62, Decent | ||
| IQ2_XS | 5.9 GB | 37.1 t/s | 2K | C44, Usable | ||
| IQ3_M | 5.9 GB | 53.9 t/s | 33K | C44, Usable | ||
Falcon 7BChat | Q3_K_S | 6.0 GB | 62.9 t/s | — | C44, Usable | |
| Q3_K_M | 6.0 GB | 53.3 t/s | 33K | C44, Usable |
Specs
- Architecture
- Ampere
- VRAM
- 6 GB GDDR6
- Memory bandwidth
- 336.0 GB/s
- FP16 (dense)
- 26.16 TFLOPS
- CUDA cores
- 3,840
- Tensor cores
- 120
- Compute capability
- 8.6
- TDP
- 115 W
- Released
- 2021-01-26
RTX 3060 Laptop GPU FAQ
What is the largest model the RTX 3060 Laptop GPU can run?
Qwen2.5 7B Instruct (7.6B) is the largest at 4-bit or better: at Q4_K_M it uses 5.8 GB of 6 GB, and runs at about 43.8 tok/s. At 3-bit, a bigger model fits too: Gemma 2 9B IT (9.2B) at IQ3_M, 5.7 GB.
Can the RTX 3060 Laptop GPU run Qwen3.5 4B?
Yes. Qwen3.5 4B fits at Q4_K_M, using 3.4 GB of 6 GB, and it is the best model that runs well on this card.
Can the RTX 3060 Laptop GPU run 14B models?
No. Qwen3 14B needs 11.9 GB at Q4, more than the 6 GB on this card.
How many tokens per second does the RTX 3060 Laptop GPU give?
About 67.0 tok/s on Qwen3.5 4B at Q4_K_M, with the first token after 3.2 s on a 4K-token prompt.
What power supply does the RTX 3060 Laptop GPU need?
Plan a 250 W PSU (2× the 115 W TDP).
Is renting cheaper than buying an RTX 3060 Laptop GPU?
An RTX 3080 Ti (12 GB) rents from $0.13/hr. With regular daily use, owning pays off.